mlsanityData to 14 Oct 2026

Learning the Domain Specific Inverse NUFFT for Accelerated Spiral MRI using Diffusion Models

arXiv 2404.12361 ↗First posted 18 Apr 2024cs.AI physics.med-phcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Deep learning methods for accelerated MRI achieve state-of-the-art results but largely ignore additional speedups possible with noncartesian sampling trajectories. To address this gap, we created a generative diffusion model-based reconstruction algorithm for multi-coil highly undersampled spiral MRI. This model uses conditioning during training as well as frequency-based guidance to ensure consistency between images and measurements. Evaluated on retrospective data, we show high quality (structural similarity > 0.87) in reconstructed images with ultrafast scan times (0.02 seconds for a 2D image). We use this algorithm to identify a set of optimal variable-density spiral trajectories and show large improvements in image quality compared to conventional reconstruction using the non-uniform fast Fourier transform. By combining efficient spiral sampling trajectories, multicoil imaging, and deep learning reconstruction, these methods could enable the extremely high acceleration factors needed for real-time 3D imaging.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 5 of its 14 references that are in the map. First posted on arXiv 18 Apr 2024.

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5 of 14 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI11 Apr
  2. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models15 Nov

Other references · 3

  1. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  2. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 3,045
  3. 2023Solving Inverse Problems with Score-Based Generative Priors learned from Noisy Datacited by 40

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